ArticleNucleic acids research2019
Evolutionary coupling analysis identifies the impact of disease-associated variants at less-conserved sites.
Article in Nucleic acids research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Traditional Foods, Oral Microbiome, and Systemic Health: Molecular Pathways Linking Nutrition and Oral Disease Prevention.International journal of molecular sciences · 2026Review
- Molecular Evolution of Paralogous Cold Shock Proteins in E. coli: A Study of Asymmetric Divergence and Protein Functional Networks.Molecular biotechnology · 2026Article
- Identification of coevolving positions by ancestral reconstruction.Communications biology · 2025Article
- Genetic analysis of non-syndromic peg lateralis using whole-exome sequencing.Frontiers in genetics · 2025Article
- PyCoM: a python library for large-scale analysis of residue-residue coevolution data.Bioinformatics (Oxford, England) · 2024Article
- LRRK2 kinase activity is necessary for development and regeneration in Nematostella vectensis.Research square · 2023Article
- Expanding the clinical and immunological phenotypes of PAX1-deficient SCID and CID patients.Clinical immunology (Orlando, Fla.) · 2023Article
- Development of bioinformatics and multi-omics analyses in organoids.BMB reports · 2023Review
- Intragenic compensation through the lens of deep mutational scanning.Biophysical reviews · 2022Review
- Network-based machine learning approach to predict immunotherapy response in cancer patients.Nature communications · 2022Article
- Compensatory epistasis explored by molecular dynamics simulations.Human genetics · 2021Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Genome-wide association studies have discovered a large number of genetic variants in human patients with the disease. Thus, predicting the impact of these variants is important for sorting disease-associated variants (DVs) from neutral variants. Current methods to predict the mutational impacts depend on evolutionary conservation at the mutation site, which is determined using homologous sequences and based on the assumption that variants at well-conserved sites have high impacts. However, many DVs at less-conserved but functionally important sites cannot be predicted by the current methods. Here, we present a method to find DVs at less-conserved sites by predicting the mutational impacts using evolutionary coupling analysis. Functionally important and evolutionarily coupled sites often have compensatory variants on cooperative sites to avoid loss of function. We found that our method identified known intolerant variants in a diverse group of proteins. Furthermore, at less-conserved sites, we identified DVs that were not identified using conservation-based methods. These newly identified DVs were frequently found at protein interaction interfaces, where species-specific mutations often alter interaction specificity. This work presents a means to identify less-conserved DVs and provides insight into the relationship between evolutionarily coupled sites and human DVs.
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